Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.