We understand that the computational framework presented in this work represents a step towards creating a more comprehensive framework that also takes into account the effects of alloying on phonon scattering, for example, by considering effective phonon band structures and by developing computationally cheaper models to directly estimate the carrier mobility in alloys, akin to the semi-empirical models developed by one of the co-authors. Looking forward, data-driven methods will play a key role in computational alloy design. Among the promising developments are the application of machine learning (ML) to design of high-entropy alloys, and the development of new ML algorithms that optimize fractional compositions. In concert with computational predictions, high-throughput bulk and thin-film synthesis will play a role in validation of the computational predictions. We may envision the synergy of first-principles alloy modeling (demonstrated in this work), data-driven methods, and high-throughput bulk synthesis to tackle the formidable challenge of alloy design and optimization.