The quasi-harmonic Debye approximation has been implemented within the AFLOW and Materials Project frameworks for high-throughput computational science (Automatic Gibbs Library, AGL), in order to calculate thermal properties such as the Debye temperature and the thermal conductivity of materials. We demonstrate that the AGL method, which is significantly cheaper computationally compared to the fully ab initio approach, can reliably predict the ordinal ranking of the thermal conductivity for several different classes of semiconductor materials. We also find that for the set of 182 materials investigated in this work the Debye temperature, calculated with the AGL, is often a better predictor of the ordinal ranking of the experimental thermal conductivities than the calculated thermal conductivity. The Debye temperature is thus a potential descriptor for high-throughput screening of the thermal properties of materials.