The presented workflow should be generally useful for organic CSP and can help address the often prohibitive costs associated with the DFT final ranking of predicted structures. For instance, training to the TTBI landscape was completed at a cost of 1498 CPU hours, which is conservatively estimated to be a 155 fold reduction compared to evaluating the landscape directly at the target (PBE-D3) level of theory. In real time, this corresponds to the difference between 20 hours and 130 days using 80 Intel(R) Xeon(R) Gold 6248 CPU cores @ 2.50GHz. Similar efficiency was observed for resorcinol with the cost of the corrected landscape estimated at 1293 CPU hours. The on-the-fly sampling is likewise notably efficient. The initial sampling, which added 1636 structures to the training set, corresponds to only 3.6% of the structures evaluated during the simulations. However, as shown in the results, if willing to accept lower accuracy a similarly robust potential can be achieved with considerably less sampling and thus cost.