Count-distinct SPARQL queries compute the number of unique values in the results of a query executed on a Knowledge Graph. However, counting the exact number of distinct values is often computationally demanding and time-consuming. As a result, these queries often fail on public SPARQL endpoints due to fair use policies. In this paper, we propose CRAWD, a new sampling-based approach designed to approximate count-distinct SPARQL queries. CRAWD significantly improves sampling efficiency and allows feasible execution of count-distinct SPARQL queries on public SPARQL endpoints, considerably improving existing methods.