We present a computational methodology for the screening of a chemical space of 10 25 substitutet NBD molecules for promising MOST systems with high energy density and TBR barrier that absorb in the visible part of the solar spectrum. We show that semiempirical tight-binding methods (SQM) can be used used to identify molecules with high energy densities and thermal back reaction (TBR) barriers as well as suitable absorption maxima by benchmarking against DFT calculations and experiment. We use the SQM methods to construct a dataset of nearly 34,000 molecules that provided even coverage of the chemical space of interest and train graph convolutional networks to predict energy densities, TBR barriers, and absorption spectra and then use the models together with a genetic algorithm to search chemical space for promising MOST candidates.