In the framework of Qualitative Spatio-Temporal Reasoning (QSTR), we can consider constraints like x \(\{\) is above \(\vee \) is under \(\}\)  y, and combinations thereof, to represent and reason about spatial or temporal information in an intuitive, human-like way. QSTR becomes particularly important in view of possible lack, uncertainty, and/or imperfection of metric data, as treating such quantitative information qualitatively would provide more leeway to perform sound reasoning. Adding to the usefulness of QSTR, in this short paper, we introduce the notion of multi-scenario for representing solutions of networks of qualitative spatio-temporal constraints in a compact manner, as a means to assessing and enhancing the explainability and robustness of AI systems that involve spatio-temporal information. Further, we prove certain theoretical properties pertaining to this novel notion, and even introduce some robustness measures relating to our notion of multi-scenario.