In this paper, we develop a framework for designing optimal organic Rankine cycle (ORC) power systems that simultaneously considers both thermodynamic and economic objectives. This methodology relies on computeraided molecular design (CAMD) techniques that allow the identification of an optimal working fluid during the thermo-economic optimization of the system. The SAFT-γ Mie equation of state is used to determine the necessary thermodynamic properties of the designed working fluids, with critical and transport properties estimated using empirical group-contribution methods. The framework is then applied to the design of sub-critical and non-recuperated ORC systems in different applications spanning a range of heat-source temperatures. When minimizing the specific investment cost (SIC) of these systems, it is found that the optimal molecular size of the working fluid is linked to the heat-source temperature, as expected, but also that the introduction of a minimum pinch point constraint that is commonly employed to account for inherent trade-offs between system performance and cost is not required. The optimal SICs of waste-heat ORC systems with heat-source temperatures of 150 °C, 250 °C and 350 °C are £10,120/kW, £4,040/kW and £2,910/kW, when employing propane, 2-butane and 2- heptene as the working fluids, respectively. During a set of MINLP optimizations of the ORC systems with heatsource temperatures of 150 °C and 250 °C, it is found that 1,3-butadiene and 4-methyl-2-pentene are the bestperforming working fluids, respectively, with SICs of £9,640/kW and £4,000/kW. These substances represent novel working fluids for ORC systems that cannot be determined a priori by specifying any working-fluid family or by following traditional methods of testing multiple fluids. Interestingly, the same molecules are identified in a multi-objective optimization considering both the total investment cost and net power output. These findings highlight the power of this approach as it enables the selection of novel working fluids while optimizing ORC systems using single or multiple thermo-economic performance indicators.